Virtual Gathering ("Coffee & Cake")

• Ralf Eichhorn (Stockholm University)
• Alberto Imparato

Understanding the friction of atomically thin layered materials

• Astrid de Wijn (Norwegian University of Science and Technology)

Friction is a ubiquitous phenomenon that greatly affects our everyday lives and is responsible for large amounts of energy loss in industrialised societies. Layered materials such as graphene have interesting frictional properties and are often used as (additives to) lubricants to reduce friction and protect against wear. Experimental Atomic Force Microscopy studies and detailed simulations have shown a number of intriguing effects such as friction strengthening and dependence of friction on the number of layers covering a surface. Here, we propose a simple, fundamental, model for friction on thin sheets. We use our model to explain a variety of seemingly contradictory experimental as well as numerical results. We also use it to study thermal effects and aging in these systems.

Quantitative analysis of non-equilibrium systems from short experiments

• Supriya Krishnamurthy (Stockholm University)

We provide a minimal strategy for the quantitative analysis of a large class of non-equilibrium systems in transient or steady state using the short-time Thermodynamic Uncertainty Relation (TUR). From short-time trajectory data obtained from numerics or experiments, we demonstrate how we can simultaneously infer quantitatively, both the thermodynamic force field acting on the system, as well as the exact rate of entropy production. We benchmark the scheme against exactly solvable models, before applying it to cases where neither analytical nor numerical results are available.

The Vernon transform and its use in quantum thermodynamics

• Erik Aurell (KTH)

Discussion Sessions

• Astrid de Wijn (Norwegian University of Science and Technology)
• Supriya Krishnamurthy (Stockholm University)
• Erik Aurell (KTH)

The unbearable lightness of single neuron non-linearities in neural network learning algorithms

• Yasser Roudi (Norwegian University of Science and Technology)

Machine Learning for Active Matter: Opportunities and Challenges

• Giovanni Volpe (Gothenburg University)

Machine-learning methods are starting to shape active-matter research. Which new trends will this start? Which new groundbreaking insight and applications can we expect? More fundamentally, what can this contribute to our understanding of active matter? Can this help us to identify unifying principles and systematise active matter? This presentation addresses some of these questions with some concrete examples, exploring how machine learning is steering active matter towards new directions, offering unprecedented opportunities and posing practical and fundamental challenges. I will illustrate some most successful recent applications of machine learning to active matter with a slight bias towards work done in my research group: enhancing data acquisition and analysis; providing new data-driven models; improving navigation and search strategies; offering insight into the emergent dynamics of active matter in crowded and complex environments. I will discuss the opportunities and challenges that are emerging: implementing feedback control; uncovering underlying principles to systematise active matter; understanding the behaviour, organisation and evolution of biological active matter; realising active matter with embodied intelligence. Finally, I will highlight how active matter and machine learning can work together for mutual benefit.

Complexity in crystal plasticity: strain burst statistics and machine learning

• Lasse Laurson (Tampere University)

In this talk I will review our recent results concerning critical-like statistics of dislocation avalanches, i.e., strain bursts observed in plastically deforming crystals, as obtained from two and three-dimensional discrete dislocation dynamics simulations. Specifically, I will discuss the “glassy” dislocation dynamics in pure crystals without any additional defects, as well as the depinning phase transition exhibited by dislocation assemblies interacting with a quenched pinning field due to other defects in the crystal such as precipitates. Moreover, I will also discuss our recent attempts to employ machine learning to quantify the degree of predictability of the plastic deformation process, which in the case of small crystals exhibits significant sample-to-sample variability.

Discussion Sessions

• Lasse Laurson (Tampere University)
• Yasser Roudi (Nordita)
• Giovanni Volpe (Gothenburg University)

Capillary control of failure in soft composite materials

• Marc Suné (Nordita)

Since Euler’s original study, the buckling instability of a column subjected to longitudinal compression forces has been extensively studied in homogeneous, isotropic, linear–elastic solids. In our work we examine the nature of the buckling in inhomogeneous soft composite materials. In particular, we consider a soft host with liquid inclusions both large and small relative to the elastocapillarity length, which lead to softening and stiffening of a homogeneous composite respectively. However, by imposing a gradient of the inclusion volume fraction or by varying the inclusion size we can deliberately manipulate the spatial structure of the composite properties of a column and thereby control the nature of Euler buckling.

The isomorph filter: identifying when an equation can be universal

• Lorenzo Costigliola (Department of Science and Environment, Roskilde University)

In the last few decades the density-scaling approach has widened our knowledge of liquids and glasses. Density scaling allows for a drastic reduction of the amount of information required to describe the structure and dynamics of a system in its phase diagram. One of the possible explanations for why density scaling holds is provided by the isomorph theory. In this theoretical framework a key point is the recognition that the physical properties of a system should be expressed in reduced units, i.e., made dimensionless using inverse density as length scale, thermal energy as energy scale and the ratio of l0 and thermal velocity as time scale. A consequence of the use of reduced units is the so-called isomorph filter which allows one to identify whether a given relation between physical properties can be universal or not. In this talk the isomorph theory and the concept of isomorph filter will be introduced in a general way and then the isomorph filter will be tested on two practical cases: describing the viscosity of simple liquids at high densities [1] and a modified version of the Stokes-Einstein relation [2]. References [1] L. Costigliola, U.R. Pedersen, D.M. Heyes, T. B. Schroder, and J.C. Dyre Communication: Simple liquids' high-density viscosity. J. Chem. Phys. 148, 081101 (2018). [2] L. Costigliola, D. M. Heyes, T. B. Schroder, and J. C. Dyre Revisiting the Stokes-Einstein relation without a hydrodynamic diameter. J. Chem. Phys. 150, 021101 (2019).

Angular dynamics of an ice crystal settling in a turbulent cloud

• Bernhard Mehlig (University of Gothenburg)

Small non-spherical particles settling in a quiescent fluid tend to orient so that their broad side faces down, because this is a stable fixed point of their angular dynamics at small particle Reynolds number. Turbulence randomises the orientations to some extent, and this affects the reflection patterns of polarised light from turbulent clouds containing ice crystals. An overdamped theory predicts that turbulence-induced fluctuations of the orientation are very small when the settling number Sv (a dimensionless measure of the settling speed) is large. At small Sv, by contrast, the overdamped theory predicts that turbulence randomises the orientations. This overdamped theory neglects the effect of particle inertia. Therefore we consider here how particle inertia affects the orientation of small crystals settling in turbulent air. We find that it can significantly increase the orientation variance, even when the Stokes number St (a dimensionless measure of particle inertia) is quite small. We identify different asymptotic parameter regimes where the tilt-angle variance is proportional to different inverse powers of Sv. We estimate parameter values for ice crystals in turbulent clouds and show that they cover several of the identified regimes. The theory predicts how the degree of alignment depends on particle size, shape and turbulence intensity, and that the strong horizontal alignment of small crystals is only possible when the turbulent energy dissipation is weak, of the order of 1cm^2/s^3 or less.

Discussion Sessions

• Lorenzo Costigliola (Department of Science and Environment, Roskilde University)
• Bernhard Mehlig (University of Gothenburg)
• Marc Suné (Nordita)